Analysis code for

Exploring new technologies for future generations:

Exploration-exploitation trade-off in an intergenerational framework

Table of Contents

• 1  Preparation
  • 1.1  Loading packages
  • 1.2  Execution environment
  • 1.3  Reading data
    • 1.3.1  Set Numerical ID for each participants (from 1 to 100)
  • 1.4  Color coordination
  • 1.5  Making Summary Efficiency
• 2  Exploration
  • 2.1  Making Summary Data
  • 2.2  Visualizing exploration and exploration for each participant
  • 2.3  Making Summary Dataset
  • 2.4  Visualizing Summary
    • 2.4.1  Bar graphs + Cloud Plots
    • 2.4.2  Bar Graph
  • 2.5  t-tests
    • 2.5.1  Preparation
    • 2.5.2  Nonrepaid(OBL) vs. Asocial(IND)
    • 2.5.3  Repaid(VER) vs. Asocial(IND)
• 3  Parameter Shifts
  • 3.1  Counting Over Exploration
  • 3.2  Length
  • 3.3  Width
  • 3.4  Thickness
• 4  Efficiency
  • 4.1  Visualization in whole trials
  • 4.2  Mean Efficiency (pointless graph)
    • 4.2.1  (Visualization)
  • 4.3  FInal trial comparison
    • 4.3.1  Repaid vs. Asocial
    • 4.3.2  Nonrepaid vs. Asocial
    • 4.3.3  (Second Generation)
    • 4.3.4  (Cumulative Cultural Evolution)
• 5  Questionnaire
  • 5.1  Strategy Proportion
  • 5.2  (Other scales)
  • 5.3  SVO
    • 5.3.1  Coding SVO
    • 5.3.2  SVO Categorization
    • 5.3.3  SVO summary
      • 5.3.3.1  By Three Category C:Prosocial, D: Defector (Proself+Competitor), N:non-classified
      • 5.3.3.2  Whether prosicial (1) or not (0)
  • 5.4  SVO + LM/GLM
    • 5.4.1  Data Merge
    • 5.4.2  Gaussian
    • 5.4.3  (Poisson)

$Note.$ The section titles with parenthesis "()" are additional analyses which were not reported in the manuscript.

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Preparation

Loading packages

library(stringr)
library(dplyr)
library(reshape)
library(ggplot2)
library(psych)

library(viridis)
library(scales)
library(rcartocolor)
library(effsize)

library(tidyverse)
library(magrittr)

library(apaTables)
library(MBESS)
library(xtable)
library(Hmisc)

Attaching package: ‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union



Attaching package: ‘reshape’


The following object is masked from ‘package:dplyr’:

    rename



Attaching package: ‘psych’


The following objects are masked from ‘package:ggplot2’:

    %+%, alpha


Loading required package: viridisLite


Attaching package: ‘viridis’


The following object is masked from ‘package:viridisLite’:

    viridis.map



Attaching package: ‘scales’


The following object is masked from ‘package:viridis’:

    viridis_pal


The following objects are masked from ‘package:psych’:

    alpha, rescale



Attaching package: ‘effsize’


The following object is masked from ‘package:psych’:

    cohen.d


── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✔ tibble  3.1.6     ✔ purrr   0.3.4
✔ tidyr   1.1.3     ✔ forcats 0.5.1
✔ readr   1.4.0     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ psych::%+%()        masks ggplot2::%+%()
✖ scales::alpha()     masks psych::alpha(), ggplot2::alpha()
✖ readr::col_factor() masks scales::col_factor()
✖ purrr::discard()    masks scales::discard()
✖ tidyr::expand()     masks reshape::expand()
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::lag()        masks stats::lag()
✖ reshape::rename()   masks dplyr::rename()

Warning message:
“package ‘magrittr’ was built under R version 4.0.5”

Attaching package: ‘magrittr’


The following object is masked from ‘package:purrr’:

    set_names


The following object is masked from ‘package:tidyr’:

    extract



Attaching package: ‘MBESS’


The following object is masked from ‘package:psych’:

    cor2cov


Loading required package: lattice

Loading required package: survival

Loading required package: Formula


Attaching package: ‘Hmisc’


The following objects are masked from ‘package:xtable’:

    label, label<-


The following object is masked from ‘package:psych’:

    describe


The following objects are masked from ‘package:dplyr’:

    src, summarize


The following objects are masked from ‘package:base’:

    format.pval, units

Execution environment

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] Hmisc_4.6-0       Formula_1.2-4     survival_3.2-7    lattice_0.20-41  
 [5] xtable_1.8-4      MBESS_4.9.2       apaTables_2.0.8   magrittr_2.0.2   
 [9] forcats_0.5.1     purrr_0.3.4       readr_1.4.0       tidyr_1.1.3      
[13] tibble_3.1.6      tidyverse_1.3.1   effsize_0.8.1     rcartocolor_2.0.0
[17] scales_1.1.1      viridis_0.5.1     viridisLite_0.4.0 psych_2.0.12     
[21] ggplot2_3.3.5     reshape_0.8.8     dplyr_1.0.7       stringr_1.4.0    

loaded via a namespace (and not attached):
 [1] nlme_3.1-149        fs_1.5.0            lubridate_1.7.10   
 [4] RColorBrewer_1.1-2  httr_1.4.2          repr_1.1.0         
 [7] tools_4.0.3         backports_1.2.1     utf8_1.2.2         
[10] R6_2.5.1            rpart_4.1-15        DBI_1.1.1          
[13] colorspace_2.0-2    nnet_7.3-14         withr_2.4.3        
[16] tidyselect_1.1.1    gridExtra_2.3       mnormt_2.0.2       
[19] compiler_4.0.3      cli_3.1.1           rvest_1.0.0        
[22] htmlTable_2.4.1     xml2_1.3.2          checkmate_2.1.0    
[25] pbdZMQ_0.3-4        digest_0.6.29       foreign_0.8-80     
[28] base64enc_0.1-3     jpeg_0.1-9          pkgconfig_2.0.3    
[31] htmltools_0.5.0     dbplyr_2.1.1        htmlwidgets_1.5.3  
[34] rlang_1.0.1         readxl_1.3.1        rstudioapi_0.13    
[37] generics_0.1.2      jsonlite_1.7.2      interp_1.0-33      
[40] Matrix_1.2-18       Rcpp_1.0.9          IRkernel_1.1.1.9000
[43] munsell_0.5.0       fansi_1.0.2         lifecycle_1.0.1    
[46] stringi_1.5.3       plyr_1.8.6          grid_4.0.3         
[49] parallel_4.0.3      crayon_1.4.2        deldir_1.0-6       
[52] IRdisplay_0.7.0     haven_2.3.1         splines_4.0.3      
[55] hms_1.1.0           tmvnsim_1.0-2       knitr_1.30         
[58] pillar_1.7.0        uuid_0.1-4          reprex_2.0.0       
[61] glue_1.6.1          evaluate_0.14       latticeExtra_0.6-30
[64] data.table_1.13.6   modelr_0.1.8        vctrs_0.3.8        
[67] png_0.1-7           cellranger_1.1.0    gtable_0.3.0       
[70] assertthat_0.2.1    xfun_0.24           broom_0.7.6        
[73] cluster_2.1.0       ellipsis_0.3.2     

Reading data

df<-read.csv("maindata.csv")

head(df)

A data.frame: 6 × 15

	ID	ChainID	Generation	ExpCondition	Age	Gender	Trial	Choice	Length	Width	Thickness	Fitness	erroredFitness	EarnedMoney	TimeStamp
	<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
1	I1	1	1	I	20	1	1	1	14	6	83	364	373	14.92	2.022012e+13
2	I1	1	1	I	20	1	2	2	19	6	83	394	373	14.92	2.022012e+13
3	I1	1	1	I	20	1	3	1	19	6	83	394	386	30.36	2.022012e+13
4	I1	1	1	I	20	1	4	2	24	6	83	421	386	30.36	2.022012e+13
5	I1	1	1	I	20	1	5	1	24	6	83	421	413	46.88	2.022012e+13
6	I1	1	1	I	20	1	6	2	29	6	83	446	413	46.88	2.022012e+13

colnames(df)

1. 'ID'
2. 'ChainID'
3. 'Generation'
4. 'ExpCondition'
5. 'Age'
6. 'Gender'
7. 'Trial'
8. 'Choice'
9. 'Length'
10. 'Width'
11. 'Thickness'
12. 'Fitness'
13. 'erroredFitness'
14. 'EarnedMoney'
15. 'TimeStamp'

dim(df)

1. 5000
2. 15

Set Numerical ID for each participants (from 1 to 100)

Color coordination

rep(c(1:100),50) %>% sort() -> NumID
df<-cbind(df, NumID)

CPalet<-c("#117733","#6699CC","#CF6677")
show_col(CPalet)

CPalet

1. '#117733'
2. '#6699CC'
3. '#CF6677'

Making Summary Efficiency

df %>% 
group_by(NumID) %>% 
  summarise(Efficiency= mean(Fitness)) -> Efficiency

df1<-df[df$Trial==1,] 
df1chosen<-df1[c("Generation","ExpCondition","ChainID", "Age", "Gender", "ID")]


cbind(
Efficiency,df1chosen) -> EfficiencyDf


 EfficiencyDf

#head(Efficiency)
#cbind(Efficiency, PptGeneration,PptCondition, PptSessionID ) 
#cbind(Efficiency, PptGeneration,PptCondition, PptSessionID ) -> EfficiencyDf

#colnames(EfficiencyDf)<-c("ID","Efficiency","Generation","ExpCondition","SessionID")
#EfficiencyDf

A data.frame: 100 × 8

	NumID	Efficiency	Generation	ExpCondition	ChainID	Age	Gender	ID
	<int>	<dbl>	<int>	<chr>	<int>	<int>	<int>	<chr>
1	1	673.64	1	I	1	20	1	I1
51	2	574.44	1	I	2	20	3	I2
101	3	690.86	1	I	3	21	3	I3
151	4	646.92	1	I	4	20	3	I4
201	5	548.62	1	I	5	20	2	I5
251	6	604.26	1	I	6	19	2	I6
301	7	696.34	1	I	7	20	3	I7
351	8	746.52	1	I	8	20	3	I8
401	9	710.72	1	I	9	21	3	I9
451	10	750.20	1	I	10	21	3	I10
501	11	677.64	1	I	11	21	3	I11
551	12	707.82	1	I	12	21	2	I12
601	13	680.68	1	I	13	21	2	I13
651	14	708.72	1	I	14	19	3	I14
701	15	736.90	1	I	15	21	3	I15
751	16	727.50	1	I	16	21	3	I16
801	17	689.64	1	I	17	20	2	I17
851	18	704.38	1	I	18	19	3	I18
901	19	677.84	1	I	19	19	3	I19
951	20	628.82	1	I	20	20	2	I20
1001	21	667.74	1	O	1	22	2	O1
1051	22	898.20	2	O	1	20	3	O1
1101	23	701.60	1	O	2	20	2	O2
1151	24	935.46	2	O	2	19	3	O2
1201	25	704.40	1	O	3	20	2	O3
1251	26	937.44	2	O	3	22	2	O3
1301	27	691.44	1	O	4	23	4	O4
1351	28	830.04	2	O	4	22	2	O4
1401	29	683.78	1	O	5	21	3	O5
1451	30	881.56	2	O	5	21	3	O5
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
3501	71	704.92	1	V	6	20	2	V6
3551	72	792.34	2	V	6	22	3	V6
3601	73	693.28	1	V	7	22	2	V7
3651	74	867.82	2	V	7	20	2	V7
3701	75	714.36	1	V	8	21	3	V8
3751	76	952.12	2	V	8	21	2	V8
3801	77	664.44	1	V	9	21	3	V9
3851	78	793.00	2	V	9	21	3	V9
3901	79	695.26	1	V	10	20	2	V10
3951	80	848.44	2	V	10	22	3	V10
4001	81	551.02	1	V	11	19	2	V11
4051	82	694.70	2	V	11	18	2	V11
4101	83	767.14	1	V	12	19	3	V12
4151	84	934.86	2	V	12	20	2	V12
4201	85	645.94	1	V	13	22	3	V13
4251	86	936.38	2	V	13	19	3	V13
4301	87	733.98	1	V	14	20	3	V14
4351	88	914.00	2	V	14	19	3	V14
4401	89	766.42	1	V	15	21	3	V15
4451	90	975.00	2	V	15	19	3	V15
4501	91	628.82	1	V	16	19	3	V16
4551	92	934.64	2	V	16	20	3	V16
4601	93	723.02	1	V	17	20	2	V17
4651	94	967.10	2	V	17	20	3	V17
4701	95	658.92	1	V	18	19	2	V18
4751	96	880.12	2	V	18	19	2	V18
4801	97	699.54	1	V	19	19	2	V19
4851	98	901.94	2	V	19	19	2	V19
4901	99	636.30	1	V	20	20	3	V20
4951	100	825.40	2	V	20	22	2	V20

EfficiencyDf$Generation - EfficiencyDf$Generation_backup

Exploration

Making Summary Data

• Counting the number of exploration for each participants

df %>% 
group_by(NumID) %>% 
  summarise(sum_choice = sum(Choice-1)) -> Exploration

df1<-df[df$Trial==1,]

PSessionID<-df1$ChainID
PGeneration<-df1$Generation
PCondition<-df1$ExpCondition
PGender<-df1$Gender



cbind(Exploration, PGeneration,PCondition, PSessionID, PGender) -> ExpDf
colnames(ExpDf)<-c("ID","Exploration","Generation","ExpCondition","SessionID","Gender")

head(ExpDf)

A data.frame: 6 × 6

	ID	Exploration	Generation	ExpCondition	SessionID	Gender
	<int>	<dbl>	<int>	<chr>	<int>	<int>
1	1	18	1	I	1	1
2	2	17	1	I	2	3
3	3	21	1	I	3	3
4	4	22	1	I	4	3
5	5	19	1	I	5	2
6	6	24	1	I	6	2

Visualizing exploration and exploration for each participant

labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unrepaid",
                         "V" = "Repaid",
                        "1"="1","2"="2","3"="3","4"="4","5"="5","6"="6","7"="7","8"="8","9"="9","10"="10","11"="11",
                        "12"="12","13"="13","14"="14",
                        "15"="15","16"="16","17"="17","18"="18","19"="19","20"="20"))

my_x_title <- expression(paste("Trial (", italic("T"), ")"))

options(repr.plot.width=10, repr.plot.height=5.2)
#options(repr.plot.width=20, repr.plot.height=20)
g<-ggplot(df[df$Generation == 1, ], aes(x=Trial, y= Choice-1, color = as.factor(ExpCondition))) + 
ylim(0,1)+
theme_bw()+geom_point()+geom_line(alpha = 1, size = 0.2)+facet_grid(as.factor(ChainID)~ExpCondition ,labeller = labeli)+
theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
      panel.grid.major.y = element_blank(),
      panel.grid.minor.y = element_blank()
     )+ xlab(my_x_title)+ 
theme(legend.position = "none")+
scale_color_manual(values = CPalet)
g

ggsave("Figures/FigExplExpr.pdf", dpi = 220, device = "pdf", width = 10, height = 5.2)
ggsave("Figures//FigExplExpr.eps", dpi = 220, device = "eps", width = 10, height = 5.2)

Making Summary Dataset

• Counting the number of exploration for each participants

DF<-ExpDf[,c(1,2,3,4)] 
head(DF)
DF %>% 
    group_by(Generation, ExpCondition) %>%
  summarise(mean = mean(Exploration), SE =　 sd(Exploration)/sqrt(20)) -> SmrDF

SmrDF$SE %>% round(.,3) -> SmrDF$SE 

SmrDF

A data.frame: 6 × 4

	ID	Exploration	Generation	ExpCondition
	<int>	<dbl>	<int>	<chr>
1	1	18	1	I
2	2	17	1	I
3	3	21	1	I
4	4	22	1	I
5	5	19	1	I
6	6	24	1	I

`summarise()` has grouped output by 'Generation'. You can override using the `.groups` argument.

A grouped_df: 5 × 4

Generation	ExpCondition	mean	SE
<int>	<chr>	<dbl>	<dbl>
1	I	20.10	0.784
1	O	19.45	1.243
1	V	24.60	1.437
2	O	11.15	1.190
2	V	10.65	1.575

#ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition!= "O"),] -> JASPVI
#ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition!= "V"),] -> JASPOI
#write.csv(JASPVI, file="JASPVI.csv", sep=",")
#write.csv(JASPOI, file="JASPOI.csv", sep=",")

Visualizing Summary

Bar graphs + Cloud Plots

y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6, repr.plot.height=3)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf[ExpDf$Generation ==1,], aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
 geom_segment(aes(x=0.6,xend=1.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=1.6,xend=2.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=2.6,xend=3.22,y=22,yend=22),linetype=2, color = "grey39", size = 0.4)+
annotate("text",x=1.3,y=0.01+12,label=taulabel) +
annotate("text",x=2.3,y=0.01+12,label=taulabel) +
annotate("text",x=3.3,y=0.01+22,label=taulabel) 
     ))

suppressMessages(
 suppressWarnings(g)
    )


 ggsave("Figures/MFreqExpo.pdf", dpi = 220, device = "pdf", width = 5, height =3)

Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”

Bar Graph

y_title <- expression(paste("Mean exploration rate (", italic("W/T"), ")"))


labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unnrepaid",
                         "V" = "Repaid",
                        "1"="First Generation","2"="Second Generation"))

options(repr.plot.width=5, repr.plot.height=3)
ggplot(SmrDF, aes(y=mean, x=ExpCondition,fill=ExpCondition))+
  geom_bar(stat = "identity", width = 0.4 , color = "black")+theme_bw()+
  geom_errorbar(aes(ymax = mean + SE, ymin = mean - SE), width = 0.1)+
                facet_grid(Generation~., labeller = labeli )+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))


#ggsave("MExpRate.pdf", dpi = 220, device = "pdf", width = 5, height =3)

t-tests

Preparation

"IND"
ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition == "I"),]$Exploration -> IND_Expo
round(psych::describe( IND_Expo),2)

"OBL"
ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition == "O"),]$Exploration -> OBL_Expo
round(psych::describe( OBL_Expo),2)

"VER"
ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition == "V"),]$Exploration -> VER_Expo
round(psych::describe( VER_Expo),2)

'IND'

A psych: 1 × 13

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	20.1	3.51	19	20	3.71	15	27	12	0.34	-1.11	0.78

'OBL'

A psych: 1 × 13

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	19.45	5.56	20	19.81	5.93	8	27	19	-0.41	-0.99	1.24

'VER'

A psych: 1 × 13

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	24.6	6.43	24	24.62	5.19	12	37	25	-0.07	-0.53	1.44

Nonrepaid(OBL) vs. Asocial(IND)

t.test(OBL_Expo, IND_Expo)
effsize::cohen.d(OBL_Expo, IND_Expo)

"Non-Repaid SD"
round(sd(IND_Expo),2)
"Asocial SD"
round(sd(VER_Expo),2)

	Welch Two Sample t-test

data:  OBL_Expo and IND_Expo
t = -0.44228, df = 32.064, p-value = 0.6613
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.643344  2.343344
sample estimates:
mean of x mean of y 
    19.45     20.10 

Cohen's d

d estimate: -0.139862 (negligible)
95 percent confidence interval:
     lower      upper 
-0.7808138  0.5010898 

'Non-Repaid SD'

3.51

'Asocial SD'

6.43

Repaid(VER) vs. Asocial(IND)

library(effsize)
t.test(VER_Expo, IND_Expo)
effsize::cohen.d(VER_Expo, IND_Expo)

"Repaid SD"
round(sd(IND_Expo),2)
"Asocial SD"
round(sd(VER_Expo),2)

	Welch Two Sample t-test

data:  VER_Expo and IND_Expo
t = 2.7485, df = 29.398, p-value = 0.01013
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 1.153453 7.846547
sample estimates:
mean of x mean of y 
     24.6      20.1 

Cohen's d

d estimate: 0.8691655 (large)
95 percent confidence interval:
    lower     upper 
0.1994516 1.5388794 

'Repaid SD'

3.51

'Asocial SD'

6.43

Parameter Shifts

df

A data.frame: 5000 × 16

ID	ChainID	Generation	ExpCondition	Age	Gender	Trial	Choice	Length	Width	Thickness	Fitness	erroredFitness	EarnedMoney	TimeStamp	NumID
<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<int>
I1	1	1	I	20	1	1	1	14	6	83	364	373	14.92	2.022012e+13	1
I1	1	1	I	20	1	2	2	19	6	83	394	373	14.92	2.022012e+13	1
I1	1	1	I	20	1	3	1	19	6	83	394	386	30.36	2.022012e+13	1
I1	1	1	I	20	1	4	2	24	6	83	421	386	30.36	2.022012e+13	1
I1	1	1	I	20	1	5	1	24	6	83	421	413	46.88	2.022012e+13	1
I1	1	1	I	20	1	6	2	29	6	83	446	413	46.88	2.022012e+13	1
I1	1	1	I	20	1	7	1	29	6	83	446	446	64.72	2.022012e+13	1
I1	1	1	I	20	1	8	2	34	6	83	467	446	64.72	2.022012e+13	1
I1	1	1	I	20	1	9	1	34	6	83	467	466	83.36	2.022012e+13	1
I1	1	1	I	20	1	10	2	34	6	78	497	466	83.36	2.022012e+13	1
I1	1	1	I	20	1	11	1	34	6	78	497	502	103.44	2.022012e+13	1
I1	1	1	I	20	1	12	2	34	6	73	525	502	103.44	2.022012e+13	1
I1	1	1	I	20	1	13	1	34	6	73	525	516	124.08	2.022012e+13	1
I1	1	1	I	20	1	14	2	34	6	68	551	516	124.08	2.022012e+13	1
I1	1	1	I	20	1	15	1	34	6	68	551	549	146.04	2.022012e+13	1
I1	1	1	I	20	1	16	2	34	6	63	574	549	146.04	2.022012e+13	1
I1	1	1	I	20	1	17	1	34	6	63	574	579	169.20	2.022012e+13	1
I1	1	1	I	20	1	18	2	34	6	58	596	579	169.20	2.022012e+13	1
I1	1	1	I	20	1	19	1	34	6	58	596	598	193.12	2.022012e+13	1
I1	1	1	I	20	1	20	2	34	6	53	615	598	193.12	2.022012e+13	1
I1	1	1	I	20	1	21	1	34	6	53	615	615	217.72	2.022012e+13	1
I1	1	1	I	20	1	22	2	34	6	48	632	615	217.72	2.022012e+13	1
I1	1	1	I	20	1	23	1	34	6	48	632	623	242.64	2.022012e+13	1
I1	1	1	I	20	1	24	2	39	6	48	651	623	242.64	2.022012e+13	1
I1	1	1	I	20	1	25	1	39	6	48	651	639	268.20	2.022012e+13	1
I1	1	1	I	20	1	26	2	39	11	48	704	639	268.20	2.022012e+13	1
I1	1	1	I	20	1	27	1	39	11	48	704	710	296.60	2.022012e+13	1
I1	1	1	I	20	1	28	1	39	11	48	704	716	325.24	2.022012e+13	1
I1	1	1	I	20	1	29	2	39	16	48	750	716	325.24	2.022012e+13	1
I1	1	1	I	20	1	30	1	39	16	48	750	745	355.04	2.022012e+13	1
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
V20	20	2	V	22	2	21	2	70	46	73	833	809	325.20	2.022021e+13	100
V20	20	2	V	22	2	22	1	70	46	73	833	840	358.80	2.022021e+13	100
V20	20	2	V	22	2	23	1	70	46	73	833	835	392.20	2.022021e+13	100
V20	20	2	V	22	2	24	2	70	41	73	827	835	392.20	2.022021e+13	100
V20	20	2	V	22	2	25	1	70	41	73	827	821	425.04	2.022021e+13	100
V20	20	2	V	22	2	26	2	70	43	73	830	821	425.04	2.022021e+13	100
V20	20	2	V	22	2	27	1	70	43	73	830	817	457.72	2.022021e+13	100
V20	20	2	V	22	2	28	1	70	43	73	830	820	490.52	2.022021e+13	100
V20	20	2	V	22	2	29	1	70	43	73	830	836	523.96	2.022021e+13	100
V20	20	2	V	22	2	30	1	70	43	73	830	831	557.20	2.022021e+13	100
V20	20	2	V	22	2	31	1	70	43	73	830	832	590.48	2.022021e+13	100
V20	20	2	V	22	2	32	1	70	43	73	830	829	623.64	2.022021e+13	100
V20	20	2	V	22	2	33	1	70	43	73	830	821	656.48	2.022021e+13	100
V20	20	2	V	22	2	34	1	70	43	73	830	834	689.84	2.022021e+13	100
V20	20	2	V	22	2	35	1	70	43	73	830	831	723.08	2.022021e+13	100
V20	20	2	V	22	2	36	1	70	43	73	830	837	756.56	2.022021e+13	100
V20	20	2	V	22	2	37	1	70	43	73	830	829	789.72	2.022021e+13	100
V20	20	2	V	22	2	38	1	70	43	73	830	824	822.68	2.022021e+13	100
V20	20	2	V	22	2	39	1	70	43	73	830	835	856.08	2.022021e+13	100
V20	20	2	V	22	2	40	1	70	43	73	830	829	889.24	2.022021e+13	100
V20	20	2	V	22	2	41	1	70	43	73	830	827	922.32	2.022021e+13	100
V20	20	2	V	22	2	42	1	70	43	73	830	828	955.44	2.022021e+13	100
V20	20	2	V	22	2	43	1	70	43	73	830	831	988.68	2.022021e+13	100
V20	20	2	V	22	2	44	1	70	43	73	830	831	1021.92	2.022021e+13	100
V20	20	2	V	22	2	45	1	70	43	73	830	830	1055.12	2.022021e+13	100
V20	20	2	V	22	2	46	1	70	43	73	830	827	1088.20	2.022021e+13	100
V20	20	2	V	22	2	47	1	70	43	73	830	826	1121.24	2.022021e+13	100
V20	20	2	V	22	2	48	1	70	43	73	830	833	1154.56	2.022021e+13	100
V20	20	2	V	22	2	49	1	70	43	73	830	833	1187.88	2.022021e+13	100
V20	20	2	V	22	2	50	1	70	43	73	830	822	1220.76	2.022021e+13	100

## Detecting Over Explorer

LengthOptimal <- 70
WidthOptimal <-48
ThicknessOptimal<-11


OverExpLength<-(df$Length > LengthOptimal)+0 
OverExpWidth<-(df$Width > WidthOptimal)+0 
OverExpThickness<-(df$Thickness < ThicknessOptimal)+0

df<-dplyr::mutate(df, OverExpLength)
df<-dplyr::mutate(df, OverExpWidth)
df<-dplyr::mutate(df, OverExpThickness)

df[(df$Length > LengthOptimal)&(df$Generation == 1),]$NumID %>% unique() -> OverLengthIDG1
df[(df$Width > WidthOptimal)&(df$Generation == 1),]$NumID %>% unique() -> OverWidthIDG1
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 1),]$NumID %>% unique() -> OverThicknessIDG1

df[(df$Length > LengthOptimal)&(df$Generation == 2),]$NumID %>% unique() -> OverLengthIDG2
df[(df$Width > WidthOptimal)&(df$Generation == 2),]$NumID %>% unique() -> OverWidthIDG2
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 2),]$NumID %>% unique() -> OverThicknessIDG2

"L, G1"
OverLengthIDG1
length(OverLengthIDG1)
"W, G1"
OverWidthIDG1
length(OverWidthIDG1)
"T, G1"
OverThicknessIDG1
length(OverThicknessIDG1)

"Total, G1"
c(OverLengthIDG1, OverWidthIDG1, OverThicknessIDG1) %>%  unique() 
c(OverLengthIDG1, OverWidthIDG1, OverThicknessIDG1) %>%  unique()  %>% length()


OverLengthIDG2
OverWidthIDG2
OverThicknessIDG2

'L, G1'

1. 4
2. 17
3. 71
4. 73
5. 83
6. 85

6

'W, G1'

1. 4
2. 15
3. 17
4. 21
5. 25
6. 61
7. 63
8. 65
9. 71
10. 73
11. 81
12. 89
13. 95
14. 97

14

'T, G1'

49

1

'Total, G1'

1. 4
2. 17
3. 71
4. 73
5. 83
6. 85
7. 15
8. 21
9. 25
10. 61
11. 63
12. 65
13. 81
14. 89
15. 95
16. 97
17. 49

17

1. 22
2. 26
3. 30
4. 38
5. 42
6. 54
7. 60
8. 72
9. 74
10. 84
11. 86
12. 96
13. 98
14. 100

1. 22
2. 26
3. 32
4. 36
5. 40
6. 44
7. 54
8. 56
9. 58
10. 60
11. 62
12. 66
13. 70
14. 72
15. 74
16. 76
17. 80
18. 82
19. 84
20. 90
21. 92
22. 96
23. 98
24. 100

1. 50
2. 74

Counting Over Exploration

"Geneartion1"
"L,W,T"
df[(df$Length > LengthOptimal)&(df$Generation == 1),]$ID %>% unique() ->OLIDG1
df[(df$Width > WidthOptimal)&(df$Generation == 1),]$ID %>% unique() ->OWIDG1
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 1),]$ID %>% unique() -> OTIDG1

OLIDG1
OWIDG1
OTIDG1
c(OLIDG1,OWIDG1,OTIDG1)%>% unique() 　-> AllIDG1
#c(OLIDG1,OWIDG1,OTIDG1)%>% unique() %>% length


OverIndNG1 <- str_detect(AllIDG1, pattern="I") %>% sum()
OverOblNG1 <- str_detect(AllIDG1, pattern="O") %>% sum()
OverVerNG1 <- str_detect(AllIDG1, pattern="V") %>% sum()


OverExpSummaryG1<-c(OverIndNG1 , OverOblNG1, OverVerNG1)
OverExpSummaryG1



"Geneartion2"
"L,W,T"
df[(df$Length > LengthOptimal)&(df$Generation == 2),]$ID %>% unique() ->OLIDG2
df[(df$Width > WidthOptimal)&(df$Generation == 2),]$ID %>% unique() ->OWIDG2
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 2),]$ID %>% unique() -> OTIDG2

OLIDG2
OWIDG2
OTIDG2
c(OLIDG2,OWIDG2,OTIDG2)%>% unique() 　-> AllIDG2
#c(OLIDG2,OWIDG2,OTIDG2)%>% unique() %>% length


OverIndNG2<- str_detect(AllIDG2, pattern="I") %>% sum()
OverOblNG2 <- str_detect(AllIDG2, pattern="O") %>% sum()
OverVerNG2 <- str_detect(AllIDG2, pattern="V") %>% sum()


OverExpSummaryG2<-c(OverIndNG2 , OverOblNG2, OverVerNG2)
OverExpSummaryG2


rbind(OverExpSummaryG1, OverExpSummaryG2) %>% data.frame() -> OverExpSummary
colnames(OverExpSummary)<-c("asocial","unrepaid","repaid")
rownames(OverExpSummary)<-c("G1","G2")
OverExpSummary
OverExpSummary/20

'Geneartion1'

'L,W,T'

1. 'I4'
2. 'I17'
3. 'V6'
4. 'V7'
5. 'V12'
6. 'V13'

1. 'I4'
2. 'I15'
3. 'I17'
4. 'O1'
5. 'O3'
6. 'V1'
7. 'V2'
8. 'V3'
9. 'V6'
10. 'V7'
11. 'V11'
12. 'V15'
13. 'V18'
14. 'V19'

'O15'

1. 3
2. 3
3. 11

'Geneartion2'

'L,W,T'

1. 'O1'
2. 'O3'
3. 'O5'
4. 'O9'
5. 'O11'
6. 'O17'
7. 'O20'
8. 'V6'
9. 'V7'
10. 'V12'
11. 'V13'
12. 'V18'
13. 'V19'
14. 'V20'

1. 'O1'
2. 'O3'
3. 'O6'
4. 'O8'
5. 'O10'
6. 'O12'
7. 'O17'
8. 'O18'
9. 'O19'
10. 'O20'
11. 'V1'
12. 'V3'
13. 'V5'
14. 'V6'
15. 'V7'
16. 'V8'
17. 'V10'
18. 'V11'
19. 'V12'
20. 'V15'
21. 'V16'
22. 'V18'
23. 'V19'
24. 'V20'

1. 'O15'
2. 'V7'

1. 0
2. 14
3. 15

A data.frame: 2 × 3

	asocial	unrepaid	repaid
	<int>	<int>	<int>
G1	3	3	11
G2	0	14	15

A data.frame: 2 × 3

	asocial	unrepaid	repaid
	<dbl>	<dbl>	<dbl>
G1	0.15	0.15	0.55
G2	0.00	0.70	0.75

OverExpLength<-(df$Length > LengthOptimal)+0 
OverExpWidth<-(df$Width > WidthOptimal)+0 
OverExpThickness<-(df$Thickness < ThicknessOptimal)+0

df

A data.frame: 5000 × 19

ID	ChainID	Generation	ExpCondition	Age	Gender	Trial	Choice	Length	Width	Thickness	Fitness	erroredFitness	EarnedMoney	TimeStamp	NumID	OverExpLength	OverExpWidth	OverExpThickness
<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<int>	<dbl>	<dbl>	<dbl>
I1	1	1	I	20	1	1	1	14	6	83	364	373	14.92	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	2	2	19	6	83	394	373	14.92	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	3	1	19	6	83	394	386	30.36	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	4	2	24	6	83	421	386	30.36	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	5	1	24	6	83	421	413	46.88	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	6	2	29	6	83	446	413	46.88	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	7	1	29	6	83	446	446	64.72	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	8	2	34	6	83	467	446	64.72	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	9	1	34	6	83	467	466	83.36	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	10	2	34	6	78	497	466	83.36	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	11	1	34	6	78	497	502	103.44	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	12	2	34	6	73	525	502	103.44	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	13	1	34	6	73	525	516	124.08	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	14	2	34	6	68	551	516	124.08	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	15	1	34	6	68	551	549	146.04	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	16	2	34	6	63	574	549	146.04	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	17	1	34	6	63	574	579	169.20	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	18	2	34	6	58	596	579	169.20	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	19	1	34	6	58	596	598	193.12	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	20	2	34	6	53	615	598	193.12	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	21	1	34	6	53	615	615	217.72	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	22	2	34	6	48	632	615	217.72	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	23	1	34	6	48	632	623	242.64	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	24	2	39	6	48	651	623	242.64	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	25	1	39	6	48	651	639	268.20	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	26	2	39	11	48	704	639	268.20	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	27	1	39	11	48	704	710	296.60	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	28	1	39	11	48	704	716	325.24	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	29	2	39	16	48	750	716	325.24	2.022012e+13	1	0	0	0
I1	1	1	I	20	1	30	1	39	16	48	750	745	355.04	2.022012e+13	1	0	0	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
V20	20	2	V	22	2	21	2	70	46	73	833	809	325.20	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	22	1	70	46	73	833	840	358.80	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	23	1	70	46	73	833	835	392.20	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	24	2	70	41	73	827	835	392.20	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	25	1	70	41	73	827	821	425.04	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	26	2	70	43	73	830	821	425.04	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	27	1	70	43	73	830	817	457.72	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	28	1	70	43	73	830	820	490.52	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	29	1	70	43	73	830	836	523.96	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	30	1	70	43	73	830	831	557.20	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	31	1	70	43	73	830	832	590.48	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	32	1	70	43	73	830	829	623.64	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	33	1	70	43	73	830	821	656.48	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	34	1	70	43	73	830	834	689.84	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	35	1	70	43	73	830	831	723.08	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	36	1	70	43	73	830	837	756.56	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	37	1	70	43	73	830	829	789.72	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	38	1	70	43	73	830	824	822.68	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	39	1	70	43	73	830	835	856.08	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	40	1	70	43	73	830	829	889.24	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	41	1	70	43	73	830	827	922.32	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	42	1	70	43	73	830	828	955.44	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	43	1	70	43	73	830	831	988.68	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	44	1	70	43	73	830	831	1021.92	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	45	1	70	43	73	830	830	1055.12	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	46	1	70	43	73	830	827	1088.20	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	47	1	70	43	73	830	826	1121.24	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	48	1	70	43	73	830	833	1154.56	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	49	1	70	43	73	830	833	1187.88	2.022021e+13	100	0	0	0
V20	20	2	V	22	2	50	1	70	43	73	830	822	1220.76	2.022021e+13	100	0	0	0

df[(df$Length > LengthOptimal)|(df$Generation==1) ,]

A data.frame: 3331 × 19

	ID	ChainID	Generation	ExpCondition	Age	Gender	Trial	Choice	Length	Width	Thickness	Fitness	erroredFitness	EarnedMoney	TimeStamp	NumID	OverExpLength	OverExpWidth	OverExpThickness
	<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<int>	<dbl>	<dbl>	<dbl>
1	I1	1	1	I	20	1	1	1	14	6	83	364	373	14.92	2.022012e+13	1	0	0	0
2	I1	1	1	I	20	1	2	2	19	6	83	394	373	14.92	2.022012e+13	1	0	0	0
3	I1	1	1	I	20	1	3	1	19	6	83	394	386	30.36	2.022012e+13	1	0	0	0
4	I1	1	1	I	20	1	4	2	24	6	83	421	386	30.36	2.022012e+13	1	0	0	0
5	I1	1	1	I	20	1	5	1	24	6	83	421	413	46.88	2.022012e+13	1	0	0	0
6	I1	1	1	I	20	1	6	2	29	6	83	446	413	46.88	2.022012e+13	1	0	0	0
7	I1	1	1	I	20	1	7	1	29	6	83	446	446	64.72	2.022012e+13	1	0	0	0
8	I1	1	1	I	20	1	8	2	34	6	83	467	446	64.72	2.022012e+13	1	0	0	0
9	I1	1	1	I	20	1	9	1	34	6	83	467	466	83.36	2.022012e+13	1	0	0	0
10	I1	1	1	I	20	1	10	2	34	6	78	497	466	83.36	2.022012e+13	1	0	0	0
11	I1	1	1	I	20	1	11	1	34	6	78	497	502	103.44	2.022012e+13	1	0	0	0
12	I1	1	1	I	20	1	12	2	34	6	73	525	502	103.44	2.022012e+13	1	0	0	0
13	I1	1	1	I	20	1	13	1	34	6	73	525	516	124.08	2.022012e+13	1	0	0	0
14	I1	1	1	I	20	1	14	2	34	6	68	551	516	124.08	2.022012e+13	1	0	0	0
15	I1	1	1	I	20	1	15	1	34	6	68	551	549	146.04	2.022012e+13	1	0	0	0
16	I1	1	1	I	20	1	16	2	34	6	63	574	549	146.04	2.022012e+13	1	0	0	0
17	I1	1	1	I	20	1	17	1	34	6	63	574	579	169.20	2.022012e+13	1	0	0	0
18	I1	1	1	I	20	1	18	2	34	6	58	596	579	169.20	2.022012e+13	1	0	0	0
19	I1	1	1	I	20	1	19	1	34	6	58	596	598	193.12	2.022012e+13	1	0	0	0
20	I1	1	1	I	20	1	20	2	34	6	53	615	598	193.12	2.022012e+13	1	0	0	0
21	I1	1	1	I	20	1	21	1	34	6	53	615	615	217.72	2.022012e+13	1	0	0	0
22	I1	1	1	I	20	1	22	2	34	6	48	632	615	217.72	2.022012e+13	1	0	0	0
23	I1	1	1	I	20	1	23	1	34	6	48	632	623	242.64	2.022012e+13	1	0	0	0
24	I1	1	1	I	20	1	24	2	39	6	48	651	623	242.64	2.022012e+13	1	0	0	0
25	I1	1	1	I	20	1	25	1	39	6	48	651	639	268.20	2.022012e+13	1	0	0	0
26	I1	1	1	I	20	1	26	2	39	11	48	704	639	268.20	2.022012e+13	1	0	0	0
27	I1	1	1	I	20	1	27	1	39	11	48	704	710	296.60	2.022012e+13	1	0	0	0
28	I1	1	1	I	20	1	28	1	39	11	48	704	716	325.24	2.022012e+13	1	0	0	0
29	I1	1	1	I	20	1	29	2	39	16	48	750	716	325.24	2.022012e+13	1	0	0	0
30	I1	1	1	I	20	1	30	1	39	16	48	750	745	355.04	2.022012e+13	1	0	0	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
4928	V20	20	1	V	20	3	28	2	54	31	83	723	704	254.44	2.022021e+13	99	0	0	0
4929	V20	20	1	V	20	3	29	2	54	36	83	742	704	254.44	2.022021e+13	99	0	0	0
4930	V20	20	1	V	20	3	30	1	54	36	83	742	743	284.16	2.022021e+13	99	0	0	0
4931	V20	20	1	V	20	3	31	2	54	41	83	755	743	284.16	2.022021e+13	99	0	0	0
4932	V20	20	1	V	20	3	32	1	54	41	83	755	757	314.44	2.022021e+13	99	0	0	0
4933	V20	20	1	V	20	3	33	2	54	41	88	722	757	314.44	2.022021e+13	99	0	0	0
4934	V20	20	1	V	20	3	34	1	54	41	88	722	724	343.40	2.022021e+13	99	0	0	0
4935	V20	20	1	V	20	3	35	2	54	41	83	755	724	343.40	2.022021e+13	99	0	0	0
4936	V20	20	1	V	20	3	36	2	54	41	78	785	724	343.40	2.022021e+13	99	0	0	0
4937	V20	20	1	V	20	3	37	1	54	41	78	785	780	374.60	2.022021e+13	99	0	0	0
4938	V20	20	1	V	20	3	38	2	54	41	73	812	780	374.60	2.022021e+13	99	0	0	0
4939	V20	20	1	V	20	3	39	1	54	41	73	812	810	407.00	2.022021e+13	99	0	0	0
4940	V20	20	1	V	20	3	40	2	54	41	68	838	810	407.00	2.022021e+13	99	0	0	0
4941	V20	20	1	V	20	3	41	1	54	41	68	838	840	440.60	2.022021e+13	99	0	0	0
4942	V20	20	1	V	20	3	42	2	59	41	68	846	840	440.60	2.022021e+13	99	0	0	0
4943	V20	20	1	V	20	3	43	1	59	41	68	846	852	474.68	2.022021e+13	99	0	0	0
4944	V20	20	1	V	20	3	44	2	59	46	68	852	852	474.68	2.022021e+13	99	0	0	0
4945	V20	20	1	V	20	3	45	1	59	46	68	852	852	508.76	2.022021e+13	99	0	0	0
4946	V20	20	1	V	20	3	46	2	64	46	68	857	852	508.76	2.022021e+13	99	0	0	0
4947	V20	20	1	V	20	3	47	1	64	46	68	857	854	542.92	2.022021e+13	99	0	0	0
4948	V20	20	1	V	20	3	48	2	64	41	68	851	854	542.92	2.022021e+13	99	0	0	0
4949	V20	20	1	V	20	3	49	1	64	41	68	851	857	577.20	2.022021e+13	99	0	0	0
4950	V20	20	1	V	20	3	50	1	64	41	68	851	855	611.40	2.022021e+13	99	0	0	0
4958	V20	20	2	V	22	2	8	2	74	51	73	831	832	100.24	2.022021e+13	100	1	1	0
4959	V20	20	2	V	22	2	9	1	74	51	73	831	836	133.68	2.022021e+13	100	1	1	0
4960	V20	20	2	V	22	2	10	2	74	51	78	803	836	133.68	2.022021e+13	100	1	1	0
4961	V20	20	2	V	22	2	11	1	74	51	78	803	792	165.36	2.022021e+13	100	1	1	0
4962	V20	20	2	V	22	2	12	1	74	51	78	803	793	197.08	2.022021e+13	100	1	1	0
4963	V20	20	2	V	22	2	13	2	74	56	78	796	793	197.08	2.022021e+13	100	1	1	0
4964	V20	20	2	V	22	2	14	1	74	56	78	796	792	228.76	2.022021e+13	100	1	1	0

Length

labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unnrepaid",
                         "V" = "Repaid",
                        "1"="First Generation","2"="Second Generation"))

options(repr.plot.width=10, repr.plot.height=10)
ggplot(df, aes(x=Trial,y=Length, group = as.factor(ID), color = as.factor(ChainID))) + 
geom_point(alpha =0.5)+  geom_line(alpha=0.6)+
geom_hline(yintercept = 70, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
guides(color=guide_legend(title="ID"))+theme(legend.position = "none")+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli) -> Lplot
Lplot

labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unrepaid",
                         "V" = "Repaid",
                        "1"="First Generation","2"="Second Generation"))

options(repr.plot.width=12, repr.plot.height=12)
ggplot(df, aes(x=Trial,y=Length, group = as.factor(ID), color = as.factor(OverExpLength))) +  
geom_point(alpha =0.5)+  geom_line(alpha=0.5)+
guides(color=guide_legend(title="Overexploration"))+
geom_hline(yintercept = 70, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli)

Width

options(repr.plot.width=10, repr.plot.height=10)
ggplot(df, aes(x=Trial,y=Width, group = as.factor(ID), color = as.factor(ChainID), alpha = 0.2)) + 
geom_point(alpha =0.5)+  geom_line(alpha=0.6)+
geom_hline(yintercept = 48, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
guides(color=guide_legend(title="ID"))+theme(legend.position = "none")+
facet_grid(ExpCondition ~ Generation,labeller = labeli) -> Wplot
Wplot

options(repr.plot.width=12, repr.plot.height=12)
ggplot(df, aes(x=Trial,y=Width, group = as.factor(ID), color = as.factor(OverExpWidth), alpha = 0.2)) +
geom_point(alpha =0.5)+  geom_line(alpha=0.5)+
guides(color=guide_legend(title="Overexploration"))+
geom_hline(yintercept = 48, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli)

Thickness

options(repr.plot.width=10, repr.plot.height=10)
ggplot(df, aes(x=Trial,y=Thickness, group = as.factor(ID), color = as.factor(ChainID))) +
geom_point(alpha =0.5)+  geom_line(alpha=0.6)+
geom_hline(yintercept = 11, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
guides(color=guide_legend(title="ID"))+theme(legend.position = "none")+
facet_grid(ExpCondition ~ Generation,labeller = labeli) -> Tplot
Tplot

options(repr.plot.width=12, repr.plot.height=12)
ggplot(df, aes(x=Trial,y=Thickness, group = as.factor(ID), color = as.factor(OverExpThickness), alpha = 0.2)) +  
geom_point(alpha =0.5)+  geom_line(alpha=0.5)+
guides(color=guide_legend(title="Overexploration"))+
geom_hline(yintercept = 11, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli)

options(repr.plot.width=12, repr.plot.height=12)
y_title <- expression("Cumulative payoff")
x_title <- expression("Trial")
labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Nonrepaid",
                         "V" = "Repaid",
                        "1"="First Geneartion",
                        "2"="Second Generation"))

#options(repr.plot.width=6., repr.plot.height=6.5)
df %>%  ggplot(aes(x=Trial,y=as.numeric(EarnedMoney/2)))+
geom_line(aes(group =ChainID, color = as.factor(ChainID)), alpha = 0.3)+
geom_point(aes(group =ChainID, color = as.factor(ChainID)), alpha = 0.5)+  
facet_grid(ExpCondition ~ Generation, labeller=labeli ) +
                      theme_bw() +theme(legend.position = "none")+
stat_summary(fun="mean", geom="line", alpha = 1, size = 1.3) +
stat_summary(fun="mean", geom="point", alpha = 1, size = 1.6)+
ylab(y_title)+xlab(x_title)+theme(text = element_text(size = 16))+
stat_summary(fun = mean,
               fun.min = function(x) mean(x) - sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               alpha = 0.8,  geom = "linerange", size =0.5)+geom_hline(yintercept = 364, size = 0.4, linetype = 5)  -> CPayoffG
CPayoffG

library(gridExtra)
options(repr.plot.width=26, repr.plot.height=12)
grid.arrange(Lplot, Wplot, Tplot, nrow = 1) -> LWTg


LWTg
ggsave("Figures/LWTshift.pdf",LWTg, dpi = 220, device = "pdf", width = 26, height = 12)

Attaching package: ‘gridExtra’


The following object is masked from ‘package:dplyr’:

    combine

TableGrob (1 x 3) "arrange": 3 grobs
  z     cells    name           grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
3 3 (1-1,3-3) arrange gtable[layout]

library(gridExtra)
options(repr.plot.width=18, repr.plot.height=18)






layout <- rbind(c(1, 2),
                 c(3, 4))
grid.arrange(Lplot, Wplot, Tplot, CPayoffG, layout_matrix =layout) -> LWTPayoffgraph
ggsave("Figures/LWTshiftPayoff.pdf", LWTPayoffgraph, dpi = 220, device = "pdf", width = 18, height = 18)

Efficiency

Visualization in whole trials

y_title <- expression("Technological efficiency")
x_title <- expression("Trial "*(italic(i)))
labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unrepaid",
                         "V" = "Repaid",
                        "1"="First Geneartion",
                        "2"="Second Generation"))

options(repr.plot.width=6., repr.plot.height=6.5)
df %>%  ggplot(aes(x=Trial,y=as.numeric(Fitness)))+
geom_line(aes(group =ChainID, color = as.factor(ChainID)), alpha = 0.3)+
geom_point(aes(group =ChainID, color = as.factor(ChainID)),size = 0.7, alpha = 0.5)+  
facet_grid(ExpCondition ~ Generation, labeller=labeli ) +
                      theme_bw() +theme(legend.position = "none")+
stat_summary(fun="mean", geom="line", alpha = 1, size = 0.5) +
stat_summary(fun="mean", geom="point", alpha = 1, size = 0.9)+
ylab(y_title)+xlab(x_title)+
stat_summary(fun = mean,
               fun.min = function(x) mean(x) - sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               alpha = 0.8,  geom = "linerange", size =0.5)
ggsave("MEfficienct.pdt", dpi = 220, device = "pdf", width = 6, height = 6.5)

Mean Efficiency (pointless graph)

(Visualization)

options(repr.plot.width=6, repr.plot.height=3)
g <- ggplot(EfficiencyDf[EfficiencyDf$Generation ==1,], aes(x =as.factor(ExpCondition), y = Efficiency,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA), ylim=c(400,800))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Nonrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))


g

options(repr.plot.width=9, repr.plot.height=3)
g <- ggplot(EfficiencyDf, aes(x =as.factor(ExpCondition), y = Efficiency,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(.~Generation, labeller = labeli )


g

FInal trial comparison

OblOffline<-df[(df$Generation == 1)&(df$ExpCondition == "O"), ]$Fitness 
VerOffline<-df[(df$Generation == 1)&(df$ExpCondition == "V"), ]$Fitness 
IndOffline<-df[(df$Generation == 1)&(df$ExpCondition == "I"), ]$Fitness

OblOfflineF<-df[(df$Generation == 1)&(df$ExpCondition == "O")&(df$Trial == 50), ]$Fitness 
VerOfflineF<-df[(df$Generation == 1)&(df$ExpCondition == "V")&(df$Trial == 50), ]$Fitness 
IndOfflineF<-df[(df$Generation == 1)&(df$ExpCondition == "I")&(df$Trial == 50), ]$Fitness 


OblOfflineF2<-df[(df$Generation == 2)&(df$ExpCondition == "O")&(df$Trial == 50), ]$Fitness 
VerOfflineF2<-df[(df$Generation == 2)&(df$ExpCondition == "V")&(df$Trial == 50), ]$Fitness

"Ind"
psych::describe(IndOfflineF) %>% round(.,2)
"Obl"
psych::describe(OblOfflineF)%>% round(.,2)
"Ver"
psych::describe(VerOfflineF)%>% round(.,2)

'Ind'

A psych: 1 × 13

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	820.25	69.02	830	824.44	57.82	667	940	273	-0.51	-0.42	15.43

'Obl'

A psych: 1 × 13

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	789.9	119.59	800	802.38	138.62	461	940	479	-0.89	0.48	26.74

'Ver'

A psych: 1 × 13

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	838.7	103.16	845.5	847.75	111.94	627	975	348	-0.53	-0.75	23.07

options(repr.plot.width=9, repr.plot.height=4)
g <- ggplot(df[df$Trial == 50,], aes(x =as.factor(ExpCondition), y =Fitness,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA),ylim=c(400,1000))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Nonrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(.~Generation,labeller = labeli)


g

options(repr.plot.width=9, repr.plot.height=4)
g <- ggplot(df[df$Trial == 50,], aes(x =as.factor(ExpCondition), y =Fitness,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA),ylim=c(0,1000))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unnrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Nonrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(.~Generation,labeller = labeli)


g

Repaid vs. Asocial

library(exactRankTests)

 Package ‘exactRankTests’ is no longer under development.
 Please consider using package ‘coin’ instead.

t.test(VerOfflineF, IndOfflineF)
t.test(log(VerOfflineF), log(IndOfflineF))
wilcox.exact(VerOfflineF, IndOfflineF,paired=F)
effsize::cohen.d(VerOfflineF, IndOfflineF)

	Welch Two Sample t-test

data:  VerOfflineF and IndOfflineF
t = 0.66478, df = 33.171, p-value = 0.5108
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -38.00408  74.90408
sample estimates:
mean of x mean of y 
   838.70    820.25 

	Welch Two Sample t-test

data:  log(VerOfflineF) and log(IndOfflineF)
t = 0.51941, df = 33.233, p-value = 0.6069
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.05271388  0.08886949
sample estimates:
mean of x mean of y 
 6.724187  6.706109 

	Exact Wilcoxon rank sum test

data:  VerOfflineF and IndOfflineF
W = 233.5, p-value = 0.3724
alternative hypothesis: true mu is not equal to 0

Cohen's d

d estimate: 0.2102214 (small)
95 percent confidence interval:
     lower      upper 
-0.4317140  0.8521568 

sd(VerOfflineF)
sd(IndOfflineF)

103.157569816889

69.0201152982917

Nonrepaid vs. Asocial

library(exactRankTests)

t.test(OblOfflineF, IndOfflineF)
t.test(log(OblOfflineF), log(IndOfflineF))
wilcox.exact(OblOfflineF, IndOfflineF,paired=F)
effsize::cohen.d(OblOfflineF, IndOfflineF)

	Welch Two Sample t-test

data:  OblOfflineF and IndOfflineF
t = -0.98299, df = 30.393, p-value = 0.3334
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -93.37118  32.67118
sample estimates:
mean of x mean of y 
   789.90    820.25 

	Welch Two Sample t-test

data:  log(OblOfflineF) and log(IndOfflineF)
t = -1.0998, df = 28.342, p-value = 0.2807
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.13381614  0.04028977
sample estimates:
mean of x mean of y 
 6.659346  6.706109 

	Exact Wilcoxon rank sum test

data:  OblOfflineF and IndOfflineF
W = 176.5, p-value = 0.5334
alternative hypothesis: true mu is not equal to 0

Cohen's d

d estimate: -0.3108495 (small)
95 percent confidence interval:
     lower      upper 
-0.9548737  0.3331747 

sd(OblOfflineF)

119.589605251504

(Second Generation)

t.test(VerOfflineF2, OblOfflineF2)
t.test(log(VerOfflineF2), log(OblOfflineF2))
wilcox.exact(VerOfflineF2, OblOfflineF2,paired=F)
effsize::cohen.d(VerOfflineF2, OblOfflineF2)

	Welch Two Sample t-test

data:  VerOfflineF2 and OblOfflineF2
t = 0.76869, df = 37.976, p-value = 0.4468
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -30.3853  67.5853
sample estimates:
mean of x mean of y 
   897.85    879.25 

	Welch Two Sample t-test

data:  log(VerOfflineF2) and log(OblOfflineF2)
t = 0.75387, df = 37.953, p-value = 0.4556
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.03576342  0.07820096
sample estimates:
mean of x mean of y 
 6.796465  6.775246 

	Exact Wilcoxon rank sum test

data:  VerOfflineF2 and OblOfflineF2
W = 232, p-value = 0.3945
alternative hypothesis: true mu is not equal to 0

Cohen's d

d estimate: 0.2430811 (small)
95 percent confidence interval:
     lower      upper 
-0.3994484  0.8856105 

(Cumulative Cultural Evolution)

t.test(OblOfflineF2-OblOfflineF)
#cohen.d(OblOfflineF2-OblOfflineF)

OblCCE<-OblOfflineF2-OblOfflineF

	One Sample t-test

data:  OblOfflineF2 - OblOfflineF
t = 4.3618, df = 19, p-value = 0.0003357
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
  46.47505 132.22495
sample estimates:
mean of x 
    89.35 

t.test(VerOfflineF2-VerOfflineF)
#cohen.d(VerOfflineF2-VerOfflineF)


VerCCE<-VerOfflineF2-VerOfflineF

	One Sample t-test

data:  VerOfflineF2 - VerOfflineF
t = 3.4751, df = 19, p-value = 0.002535
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 23.52447 94.77553
sample estimates:
mean of x 
    59.15 

t.test(VerCCE,OblCCE)

	Welch Two Sample t-test

data:  VerCCE and OblCCE
t = -1.1339, df = 36.767, p-value = 0.2642
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -84.17601  23.77601
sample estimates:
mean of x mean of y 
    59.15     89.35 

---

---

Questionnaire

datad<-read.csv("PostQuestionData.csv")

colnames(datad)

1. 'Header'
2. 'ID'
3. 'day'
4. 'time'
5. 'session'
6. 'generation'
7. 'condition'
8. 'PC'
9. 'Str_turn'
10. 'Str_switch'
11. 'ignore_cost'
12. 'overspent'
13. 'more_spent'
14. 'maxim'
15. 'Generation'
16. 'FutureThink'
17. 'Ancestor'
18. 'Return'
19. 'SV1'
20. 'SV2'
21. 'SV3'
22. 'SV4'
23. 'SV5'
24. 'SV6'
25. 'SV7'
26. 'SV8'
27. 'SV9'

Strategy Proportion

datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), Str_turn= 2-mean(Str_turn),  Str_switch= 2-mean(Str_switch))


datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), Str_turn= (2-mean(Str_turn))*20,  Str_switch= (2-mean(Str_switch))*20)

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

A grouped_df: 5 × 5

condition	generation	n	Str_turn	Str_switch
<chr>	<int>	<int>	<dbl>	<dbl>
I	1	20	0.85	0.80
O	1	20	0.85	0.65
O	2	20	0.75	1.00
V	1	20	0.80	0.40
V	2	20	0.65	0.95

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

A grouped_df: 5 × 5

condition	generation	n	Str_turn	Str_switch
<chr>	<int>	<int>	<dbl>	<dbl>
I	1	20	17	16
O	1	20	17	13
O	2	20	15	20
V	1	20	16	8
V	2	20	13	19

(Other scales)

datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), Mingore_cost=round(mean (ignore_cost, na.rm = TRUE),2),
                SDingore_cost=round(sd(ignore_cost, na.rm = TRUE),2),
                 Moverspent=mean(overspent), 
                 SDoverspent=sd(overspent)%>%round(2), 
                 Mmorespent=mean(more_spent), 
                 SDmorespent=sd(more_spent)%>%round(2), 
                 Mmaximize=mean(maxim), 
                 SDmaximize=sd(maxim)%>%round(2))
                 


datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), 
                 MFutureThink=mean(FutureThink),
                 SFutureThink=sd(FutureThink)%>%round(2), 
                 MAncestor=mean(Ancestor),
                 SAncestor=sd(Ancestor)%>%round(2),
                 MReturn=mean(Return),
                 SReturn=sd(Return)%>%round(2))

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

A grouped_df: 5 × 11

condition	generation	n	Mingore_cost	SDingore_cost	Moverspent	SDoverspent	Mmorespent	SDmorespent	Mmaximize	SDmaximize
<chr>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
I	1	20	3.10	1.94	3.15	1.73	5.05	1.36	2.60	1.50
O	1	20	3.84	1.68	4.40	1.90	4.50	1.76	3.30	1.56
O	2	20	3.85	1.66	4.90	1.92	5.45	1.57	1.80	1.15
V	1	20	2.60	1.60	4.50	2.04	4.50	2.01	2.15	1.50
V	2	20	4.20	2.07	4.85	2.16	5.75	1.21	2.55	1.43

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

A grouped_df: 5 × 9

condition	generation	n	MFutureThink	SFutureThink	MAncestor	SAncestor	MReturn	SReturn
<chr>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
I	1	20	NA	NA	NA	NA	NA	NA
O	1	20	4.5	1.61	NA	NA	NA	NA
O	2	20	NA	NA	2.75	1.77	4.1	1.55
V	1	20	2.8	1.94	NA	NA	NA	NA
V	2	20	NA	NA	2.20	1.32	2.3	1.53

SVO

Coding SVO

PROSOC_KEY<-c(3,2,1,3,2,1,1,3,2)
PROSELF_KEY<-c(2,1,3,2,1,3,2,1,3)
COMP_KEY    <-c(1,3,2,1,3,2,3,2,1)

SVO<-datad[,(dim(datad)[2]-8):dim(datad)[2]]
PSocScore<-apply(SVO==matrix(PROSOC_KEY,dim(SVO)[1],dim(SVO)[2],T) ,1, sum)
PSelfScore<-apply(SVO==matrix(PROSELF_KEY,dim(SVO)[1],dim(SVO)[2],T) ,1, sum)
CompScore<-apply(SVO==matrix(COMP_KEY,dim(SVO)[1],dim(SVO)[2],T) ,1, sum)

datadp<-mutate(datad, PSocScore,PSelfScore,CompScore)

#mean(datadp[(datadp$condition=="I")&(datadp$generation==1),]$PSocScore)
#mean(datadp[(datadp$condition=="O")&(datadp$generation==1),]$PSocScore)
#mean(datadp[(datadp$condition=="V")&(datadp$generation==1),]$PSocScore)
#mean(datadp[(datadp$condition=="O")&(datadp$generation==2),]$PSocScore)
#mean(datadp[(datadp$condition=="V")&(datadp$generation==2),]$PSocScore)

SVO Categorization

SVO_cat<-numeric(dim(datad)[1])
SVO_cat[PSocScore > 5.9]<-"C"
SVO_cat[PSocScore < 5.9]<-"N"
SVO_cat[9-PSocScore > 5.9]<-"D"
#SVO_cat

IfSocial<-numeric(dim(datad)[1])
IfSocial[PSocScore > 5.9]<-1

datadp<-mutate(datadp, SVO_cat,IfSocial)

SVO summary

datadp %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), psoc_m = mean(PSocScore), psoc_sd = round(sd(PSocScore), 2),
          pself_m = mean(PSelfScore), pself_sd = round(sd(PSelfScore),2), 
          comp_m = mean(CompScore), comp_sd = round(sd(CompScore),2))　-> table_svo

table_svo

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

A grouped_df: 5 × 9

condition	generation	n	psoc_m	psoc_sd	pself_m	pself_sd	comp_m	comp_sd
<chr>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
I	1	20	4.85	3.62	4.15	3.62	0.00	0.00
O	1	20	5.00	4.34	3.95	4.29	0.05	0.22
O	2	20	4.75	3.95	4.20	3.93	0.00	0.00
V	1	20	4.55	4.07	4.40	4.07	0.05	0.22
V	2	20	4.25	4.27	4.70	4.22	0.05	0.22

#latex(table_svo, file = "table_svo_dexc.tex")

By Three Category C:Prosocial, D: Defector (Proself+Competitor), N:non-classified

(table(datadp[datadp$generation ==1,]$condition, datadp[datadp$generation ==1,]$SVO_cat)) ->G1SVO
(G1SVO)
(G1SVO)/20

   
     C  D  N
  I 10  9  1
  O 11  8  1
  V  9  9  2

   
       C    D    N
  I 0.50 0.45 0.05
  O 0.55 0.40 0.05
  V 0.45 0.45 0.10

(table(datadp[datadp$generation ==2,]$condition, datadp[datadp$generation ==2,]$SVO_cat)) ->G2SVO
(G2SVO)
(G2SVO)/20

   
     C  D  N
  O 10 10  0
  V  9 10  1

   
       C    D    N
  O 0.50 0.50 0.00
  V 0.45 0.50 0.05

Whether prosicial (1) or not (0)

(table(datadp[datadp$generation ==1,]$condition, datadp[datadp$generation ==1,]$IfSocial))

   
     0  1
  I 10 10
  O  9 11
  V 11  9

(table(datadp[datadp$generation ==1,]$condition, datadp[datadp$generation ==1,]$IfSocial)) ->G1IfSocial
G1IfSocial
G1IfSocial/20

   
     0  1
  I 10 10
  O  9 11
  V 11  9

   
       0    1
  I 0.50 0.50
  O 0.45 0.55
  V 0.55 0.45

SVO + LM/GLM

Data Merge

ExpDfSVO<-mutate(ExpDf, SVO_cat, IfSocial,PSocScore)
filter(ExpDfSVO, Generation == 1) -> ExpDfSVOG1

head(ExpDfSVO)

A data.frame: 6 × 9

	ID	Exploration	Generation	ExpCondition	SessionID	Gender	SVO_cat	IfSocial	PSocScore
	<int>	<dbl>	<int>	<chr>	<int>	<int>	<chr>	<dbl>	<int>
1	1	18	1	I	1	1	C	1	6
2	2	17	1	I	2	3	D	0	1
3	3	21	1	I	3	3	D	0	0
4	4	22	1	I	4	3	C	1	8
5	5	19	1	I	5	2	C	1	8
6	6	24	1	I	6	2	C	1	9

Gaussian

Gaus_additive<-lm(data = ExpDfSVOG1, Exploration~ExpCondition+IfSocial)
summary(Gaus_additive)

Call:
lm(formula = Exploration ~ ExpCondition + IfSocial, data = ExpDfSVOG1)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.3287  -2.8776  -0.3287   3.8656  12.1780 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    20.3466     1.3821  14.721   <2e-16 ***
ExpConditionO  -0.6253     1.6927  -0.369   0.7132    
ExpConditionV   4.4753     1.6927   2.644   0.0106 *  
IfSocial       -0.4933     1.3856  -0.356   0.7232    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.348 on 56 degrees of freedom
Multiple R-squared:  0.1657,	Adjusted R-squared:  0.1211 
F-statistic: 3.709 on 3 and 56 DF,  p-value: 0.01666

$Y_i = \beta_0 + \beta_1X_{unrepaid} + \beta_2X_{repaid} + \beta_3X_{prosicail} + \varepsilon$

Gaus_additive_scale<-lm(data = ExpDfSVOG1, scale(Exploration)~ExpCondition+IfSocial)
summary(Gaus_additive_scale)

options(digits=2)
summary(Gaus_additive_scale)

Call:
lm(formula = scale(Exploration) ~ ExpCondition + IfSocial, data = ExpDfSVOG1)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.16108 -0.50442 -0.05762  0.67761  2.13467 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept)   -0.18172    0.24227  -0.750   0.4564  
ExpConditionO -0.10961    0.29672  -0.369   0.7132  
ExpConditionV  0.78448    0.29672   2.644   0.0106 *
IfSocial      -0.08647    0.24288  -0.356   0.7232  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9375 on 56 degrees of freedom
Multiple R-squared:  0.1657,	Adjusted R-squared:  0.1211 
F-statistic: 3.709 on 3 and 56 DF,  p-value: 0.01666

Call:
lm(formula = scale(Exploration) ~ ExpCondition + IfSocial, data = ExpDfSVOG1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1611 -0.5044 -0.0576  0.6776  2.1347 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept)    -0.1817     0.2423   -0.75    0.456  
ExpConditionO  -0.1096     0.2967   -0.37    0.713  
ExpConditionV   0.7845     0.2967    2.64    0.011 *
IfSocial       -0.0865     0.2429   -0.36    0.723  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.94 on 56 degrees of freedom
Multiple R-squared:  0.166,	Adjusted R-squared:  0.121 
F-statistic: 3.71 on 3 and 56 DF,  p-value: 0.0167

apa.reg.table(Gaus_additive_scale)

Regression results using scale(Exploration) as the criterion
 

     Predictor     b      b_95%_CI sr2  sr2_95%_CI             Fit
   (Intercept) -0.18 [-0.67, 0.30]                                
 ExpConditionO -0.11 [-0.70, 0.48] .00 [-.02, .02]                
 ExpConditionV 0.78*  [0.19, 1.38] .10 [-.04, .25]                
      IfSocial -0.09 [-0.57, 0.40] .00 [-.02, .02]                
                                                        R2 = .166*
                                                   95% CI[.01,.31]
                                                                  

Note. A significant b-weight indicates the semi-partial correlation is also significant.
b represents unstandardized regression weights. 
sr2 represents the semi-partial correlation squared.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
 

(Poisson)

Pois_additive<-glm(data = ExpDfSVOG1, Exploration~ExpCondition+IfSocial, family = poisson(link="identity"))
Pois_additive_log<-glm(data = ExpDfSVOG1, Exploration~ExpCondition+IfSocial, family = poisson(link="log"))
summary(Pois_additive)

summary(Pois_additive_log)


#apa.reg.table(Pois_additive)

Call:
glm(formula = Exploration ~ ExpCondition + IfSocial, family = poisson(link = "identity"), 
    data = ExpDfSVOG1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9173  -0.6437  -0.0827   0.8330   2.2941  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)     20.275      1.169   17.35   <2e-16 ***
ExpConditionO   -0.639      1.408   -0.45   0.6498    
ExpConditionV    4.467      1.496    2.99   0.0028 ** 
IfSocial        -0.335      1.192   -0.28   0.7788    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 91.645  on 59  degrees of freedom
Residual deviance: 77.163  on 56  degrees of freedom
AIC: 377.4

Number of Fisher Scoring iterations: 5

Call:
glm(formula = Exploration ~ ExpCondition + IfSocial, family = poisson(link = "log"), 
    data = ExpDfSVOG1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9066  -0.6570  -0.0586   0.8465   2.2697  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)     3.0122     0.0570   52.80   <2e-16 ***
ExpConditionO  -0.0317     0.0712   -0.45   0.6559    
ExpConditionV   0.2009     0.0673    2.99   0.0028 ** 
IfSocial       -0.0231     0.0560   -0.41   0.6805    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 91.645  on 59  degrees of freedom
Residual deviance: 77.071  on 56  degrees of freedom
AIC: 377.3

Number of Fisher Scoring iterations: 4

AIC(Gaus_additive,Gaus_additive_scale,Pois_additive,Pois_additive_log)

A data.frame: 4 × 2

	df	AIC
	<dbl>	<dbl>
Gaus_additive	5	377
Gaus_additive_scale	5	168
Pois_additive	4	377
Pois_additive_log	4	377

gression results using Exploration as the criterion

 Predictor       b       b_95%_CI sr2  sr2_95%_CI           Fit

(Intercept) 20.35* [17.58, 23.12]
ExpConditionO -0.63 [-4.02, 2.77] .00 [-.02, .02]
ExpConditionV 4.48 [1.08, 7.87] .10 [-.04, .25]
IfSocial -0.49 [-3.27, 2.28] .00 [-.02, .02]
R2 = .166* 95% CI[NA,NA]

Note. A significant b-weight indicates the semi-partial correlation is also significant. b represents unstandardized regression weights. sr2 represents the semi-partial correlation squared. Square brackets are used to enclose the lower and upper limits of a confidence interval.

• indicates p < .05. ** indicates p < .01.